Translating a single word Neural Networks
The training examples are not mentioned, but, assuming the assignment involves supervised learning, normalization is, in this case, mapping the inputs and outputs to binary values.

Translation, in this simplified context, is a map of strings to strings, which isn't really an AI device by contemporary standards. Since an artificial network is given as a requirement for the solution, the goal is to train the network to act like a hash map.

$$ \Big\{..., \;
\big(
[\text{"beer", "birra", "cervexa"}]:
\text{"pivo"}
\big)
, \; ...\Big\} $$

$$ \Downarrow $$

$$ \Big\{..., \; \big(\text{"beer" : "pivo"}), (\text{"birra" : "pivo"}), (\text{"cervexa" : "pivo"}\big), \; ...\Big\} $$

Assuming there is a list of pairs $(\mathcal{X}, \mathcal{Y})$, where $\mathcal{X}$ is the word in any of the source languages and $\mathcal{Y}$ is the word in the target language, and the vocabulary of both is known to have word counts of $X$ and $Y$ respectively, one can determine the input and output layer widths (cell counts) $w_1$ and $w_{\ell}$, where $\ell$ is the number of network layers.

$$w_1 = \text{ceiling} ( \log_2{X} )$$
 $$w_{\ell} = \text{ceiling} ( \log_2{Y} )$$

The assignment of source words to binary values is arbitrary. You can sort them by language and then by whatever common character encoding used (such as UTF-8) just for debugging purposes. The same is true of target words.

A simple MLP (multi-layer perceptron) with mini-batch training using basic propagation map be appropriate.